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THE ROLE OF PROCEDURAL LEARNING ABILITY IN AUTOMATIZATION OF L2 MORPHOLOGY UNDER DIFFERENT LEARNING SCHEDULES

AN EXPLORATORY STUDY

Published online by Cambridge University Press:  10 August 2017

Yuichi Suzuki
Yuichi Suzuki*
Affiliation:
Kanagawa University
*
*Correspondence concerning this article should be addressed to Yuichi Suzuki, Faculty of Foreign Languages, Kanagawa University, 3-27-1, Rokkakubashi, Kanagawa-ku, Yokohama-shi, Kanagawa, 221-8686, Japan. E-mail: szky819@kanagawa-u.ac.jp
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Abstract

This paper reports on the reanalysis of Suzuki’s (2017) experiment and investigated the extent to which learning schedules influence automatization of second language (L2) morphology. Sixty participants were separated into two groups, which studied morphological rules for oral production under short-spacing (3.3-day intervals) and long-spacing learning conditions (7-day intervals). Their oral production test performance resulted in two measures of automatization: reaction time (RT) as an index of speedup and coefficient of variance (CV) as an index of stability/restructuring. The results showed that, while RT of both groups declined significantly after the training, the 3.3-day group exhibited greater propensity for restructuring than the 7-day group. Furthermore, procedural learning ability measured by the Tower of London task was significantly associated with RT, but not with CV, in the 3.3-day group only. These findings suggest that learning schedules and procedural learning ability influence different stages of automatization of L2 morphological learning.

Type
Research Report
Information
Studies in Second Language Acquisition , Volume 40 , Issue 4 , December 2018 , pp. 923 - 937
Copyright
Copyright © Cambridge University Press 2017 

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Footnotes

This study was supported by Grant-in-Aid for Scientific Research (KAKENHI) from Japan Society for the Promotion of Science (JSPS). I am grateful to my RAs, Naoko Miyauchi, Yuki Aizawa, Misaki Kuratsubo, Yuka Oishi, and Aya Mizuno for their assistance in data collection and coding. I would like to express my gratitude to Kara Morgan-Short for allowing me to use the TOL task.

References

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